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Psychosocial consequences of false positives in the Danish Lung Cancer CT Screening Trial: a nested matched cohort study.

ABSTRACT: OBJECTIVES:Lung cancer CT screening can reduce lung cancer mortality, but high false-positive rates may cause adverse psychosocial consequences. The aim was to analyse the psychosocial consequences of false-positive lung cancer CT screening using the lung cancer screening-specific questionnaire, Consequences of Screening in Lung Cancer (COS-LC). DESIGN AND SETTING:This study was a matched cohort study, nested in the randomised Danish Lung Cancer Screening Trial (DLCST). PARTICIPANTS:Our study included all 130 participants in the DLCST with positive CT results in screening rounds 2-5, who had completed the COS-LC questionnaire. Participants were split into a true-positive and a false-positive group and were then matched 1:2 with a control group (n=248) on sex, age (±3 years) and the time of screening for the positive CT groups or clinic visit for the control group. The true positives and false positives were also matched 1:2 with participants with negative CT screening results (n=252). PRIMARY OUTCOMES:Primary outcomes were psychosocial consequences measured at five time points. RESULTS:False positives experienced significantly more negative psychosocial consequences in seven outcomes at 1 week and in three outcomes at 1 month compared with the control group and the true-negative group (mean ? score >0 and p<0.001). True positives experienced significantly more negative psychosocial consequences in one outcome at 1 week (mean ? score 2.86 (95% CI 1.01 to 4.70), p=0.0024) and in five outcomes at 1 month (mean ? score >0 and p<0.004) compared with the true-negative group and the control group. No long-term psychosocial consequences were identified either in false positives or true positives. CONCLUSIONS:Receiving a false-positive result in lung cancer screening was associated with negative short-term psychosocial consequences. These findings contribute to the evidence on harms of screening and should be taken into account when considering implementation of lung cancer screening programmes. TRIAL REGISTRATION NUMBER:NCT00496977.

SUBMITTER: Rasmussen JF

PROVIDER: S-EPMC7279658 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Psychosocial consequences of false positives in the Danish Lung Cancer CT Screening Trial: a nested matched cohort study.

Rasmussen Jakob Fraes JF Siersma Volkert V Malmqvist Jessica J Brodersen John J

BMJ open 20200604 6

<h4>Objectives</h4>Lung cancer CT screening can reduce lung cancer mortality, but high false-positive rates may cause adverse psychosocial consequences. The aim was to analyse the psychosocial consequences of false-positive lung cancer CT screening using the lung cancer screening-specific questionnaire, Consequences of Screening in Lung Cancer (COS-LC).<h4>Design and setting</h4>This study was a matched cohort study, nested in the randomised Danish Lung Cancer Screening Trial (DLCST).<h4>Partici ...[more]

PMID: 32503869

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Project description:OBJECTIVES:We investigated if psychosocial status, sociodemographics and smoking status affected non-attendance in the control group in the randomised Danish Lung Cancer Screening Trial (DLCST). DESIGN AND SETTING:This study was an observational study nested in the DLCST. Due to large non-attendance in the control group in the second screening round we made an additional effort to collect questionnaire data from non-attenders in this group in the third screening round. We used a condition-specific questionnaire to assess psychosocial status. We analysed the differences in psychosocial status in the third and preceding rounds between non-attenders and attenders in the control group in multivariable linear regression models adjusted for sociodemographics and smoking status reported at baseline. Differences in sociodemographics and smoking status were analysed with ?2 tests (categorical variables) and t-tests (continuous variables). PRIMARY OUTCOME MEASURE:Primary outcome was psychosocial status. PARTICIPANTS:All control persons participating in the third screening round in the DLCST were included. RESULTS:Non-attenders in the third round had significantly worse psychosocial status than attenders in the scales: 'behaviour' 0.77 (99% CI 0.18 to 1.36), 'self-blame' 0.59 (99% CI 0.14 to 1.04), 'focus on airway symptoms' 0.22 (99% CI 0.08 to 0.36), 'stigmatisation' 0.51 (99% CI 0.16 to 0.86), 'introvert' 0.56 (99% CI 0.23 to 0.89) and 'harms of smoking' 0.35 (99% CI 0.11 to 0.59). Moreover, non-attenders had worse scores than attendees in the preceding screening rounds. Non-attenders also reported worse sociodemographics at baseline. CONCLUSIONS:Non-attenders had a significantly worse psychosocial status and worse sociodemographics compared with attenders. The results of our study contribute with evidence of non-response and attrition driven by psychosocial status, which in turn may be influenced by the screening intervention itself. This can be used to adjust cancer screening trial results for bias due to differential non-attendance. TRIAL REGISTRATION NUMBER:Clinicaltrials.gov Protocol Registration System (NCT00496977).

Project description:BackgroundA large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation.ResultsFor all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results.ConclusionsOf the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

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Psychosocial consequences of false positives in the Danish Lung Cancer CT Screening Trial: a nested matched cohort study.

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Psychosocial consequences of false positives in the Danish Lung Cancer CT Screening Trial: a nested matched cohort study.

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